AI Summary

• The paper discusses the concept of agentic AI and its applications in various domains. [3]
• Agentic AI refers to autonomous intelligence that can perform complex tasks and make decisions on its own. [3]
• Agentic AI: Autonomous intelligence that can perform complex tasks and make decisions on its own. [3]
• The authors propose a framework for integrating large language models (LLMs) with blockchain smart contracts using Model Context Protocol (MCP). [2]
• The paper highlights the need for evaluating AI reasoning models in pediatric medicine and discusses the comparative analysis of O3-Mini and O3-Mini-High models. [1]

Abstract
Agentic AI marks a major shift in how autonomous systems reason, plan, and execute multi-step tasks. Unlike traditional single model prompting, agentic workflows integrate multiple specialized agents with different Large Language Models(LLMs), tool-augmented capabilities, orchestration logic, and external system interactions to form dynamic pipelines capable of autonomous decision-making and action. As adoption accelerates across industry and research, organizations face a central challenge: how to design, engineer, and operate production-grade agentic AI workflows that are reliable, observable, maintainable, and aligned with safety and governance requirements. This paper provides a practical, end-to-end guide for designing, developing, and deploying production-quality agentic AI systems. We introduce a structured engineering lifecycle encompassing workflow decomposition, multi-agent design patterns, Model Context Protocol(MCP), and tool integration, deterministic orchestration, Responsible-AI considerations, and environment-aware deployment strategies. We then present nine core best practices for engineering production-grade agentic AI workflows, including tool-first design over MCP, pure-function invocation, single-tool and single-responsibility agents, externalized prompt management, Responsible-AI-aligned model-consortium design, clean separation between workflow logic and MCP servers, containerized deployment for scalable operations, and adherence to the Keep it Simple, Stupid (KISS) principle to maintain simplicity and robustness. To demonstrate these principles in practice, we present a comprehensive case study: a multimodal news-analysis and media-generation workflow. By combining architectural guidance, operational patterns, and practical implementation insights, this paper offers a foundational reference to build robust, extensible, and production-ready agentic AI workflows.

Why we think this paper is great for you:
This paper directly addresses the development of AI systems, aligning with your interest in AI applications. Understanding agentic workflows will be valuable for building intelligent solutions within your field.

AI Summary

• Bayesian statistics are the natural framework for AI risk, allowing risk estimates to be continuously updated as models are evaluated and new behaviors are observed. [3]
• Bayesian Networks (BNs) are graphical models that help represent and quantify probabilistic relationships among a set of variables, making them excellent for modeling pathways to harm that involve multiple interacting factors. [3]
• Copulas are a powerful tool for modeling statistical interdependence without assuming causality, useful for modeling systemic or cascading risks where the failure of one component correlates with the failure of others. [3]
• Monte Carlo simulation: A computational technique that models uncertainty by running thousands of trials, sampling from probability distributions to generate a range of possible outcomes and their frequencies. [3]
• Bayesian statistics: A framework for updating prior knowledge (expert belief) with new evidence, enabling learning and continuous risk estimation. [3]
• Bayesian Networks (BNs): Graphical models representing probabilistic relationships among variables, integrating expert knowledge with data to model complex causal chains and update probabilities as new evidence becomes available. [3]
• Copulas: Functions that separate marginal probability distributions from the structure of statistical interdependence, useful for modeling systemic or cascading risks. [3]
• The lack of historical data for novel AI capabilities and failure modes is a significant challenge in quantifying AI risk. [2]
• Expert elicitation: A method used when empirical data is sparse, involving querying specialists on the likelihood of specific events. [1]

Abstract
Rapidly advancing artificial intelligence (AI) systems introduce novel, uncertain, and potentially catastrophic risks. Managing these risks requires a mature risk-management infrastructure whose cornerstone is rigorous risk modeling. We conceptualize AI risk modeling as the tight integration of (i) scenario building$-$causal mapping from hazards to harms$-$and (ii) risk estimation$-$quantifying the likelihood and severity of each pathway. We review classical techniques such as Fault and Event Tree Analyses, FMEA/FMECA, STPA and Bayesian networks, and show how they can be adapted to advanced AI. A survey of emerging academic and industry efforts reveals fragmentation: capability benchmarks, safety cases, and partial quantitative studies are valuable but insufficient when divorced from comprehensive causal scenarios. Comparing the nuclear, aviation, cybersecurity, financial, and submarine domains, we observe that every sector combines deterministic guarantees for unacceptable events with probabilistic assessments of the broader risk landscape. We argue that advanced-AI governance should adopt a similar dual approach and that verifiable, provably-safe AI architectures are urgently needed to supply deterministic evidence where current models are the result of opaque end-to-end optimization procedures rather than specified by hand. In one potential governance-ready framework, developers conduct iterative risk modeling and regulators compare the results with predefined societal risk tolerance thresholds. The paper provides both a methodological blueprint and opens a discussion on the best way to embed sound risk modeling at the heart of advanced-AI risk management.

Why we think this paper is great for you:
Given the increasing focus on responsible AI, this paper offers insights into managing the risks associated with advanced AI systems. It's relevant to ensuring the safe and effective implementation of AI solutions.

Abstract
Most reinforcement learning(RL)-based methods for drone racing target fixed, obstacle-free tracks, leaving the generalization to unknown, cluttered environments largely unaddressed. This challenge stems from the need to balance racing speed and collision avoidance, limited feasible space causing policy exploration trapped in local optima during training, and perceptual ambiguity between gates and obstacles in depth maps-especially when gate positions are only coarsely specified. To overcome these issues, we propose a two-phase learning framework: an initial soft-collision training phase that preserves policy exploration for high-speed flight, followed by a hard-collision refinement phase that enforces robust obstacle avoidance. An adaptive, noise-augmented curriculum with an asymmetric actor-critic architecture gradually shifts the policy's reliance from privileged gate-state information to depth-based visual input. We further impose Lipschitz constraints and integrate a track-primitive generator to enhance motion stability and cross-environment generalization. We evaluate our framework through extensive simulation and ablation studies, and validate it in real-world experiments on a computationally constrained quadrotor. The system achieves agile flight while remaining robust to gate-position errors, developing a generalizable drone racing framework with the capability to operate in diverse, partially unknown and cluttered environments. https://yufengsjtu.github.io/MasterRacing.github.io/

Why we think this paper is great for you:
This research tackles the challenge of creating adaptable AI systems, a key component of robust product development. The focus on handling complex environments is highly pertinent to your interests.

AI Summary

• The paper discusses the development of language models that can learn from children's interactions with the world. [3]
• Researchers have created various benchmarks and datasets to evaluate the performance of these models. [3]
• The development of language models that can learn from children's interactions with the world has made significant progress in recent years. [3]
• Researchers have created various benchmarks and datasets to evaluate the performance of these models. [3]
• Multimodal learning is a key aspect of this research, as it allows models to learn from multiple sources of information. [3]
• Some notable papers include those on BabyLM, Devbench, and KiV A, which focus on developing models that can understand and generate human-like language. [2]

Abstract
Early children's developmental trajectories set up a natural goal for sample-efficient pretraining of vision foundation models. We introduce BabyVLM-V2, a developmentally grounded framework for infant-inspired vision-language modeling that extensively improves upon BabyVLM-V1 through a longitudinal, multifaceted pretraining set, a versatile model, and, most importantly, DevCV Toolbox for cognitive evaluation. The pretraining set maximizes coverage while minimizing curation of a longitudinal, infant-centric audiovisual corpus, yielding video-utterance, image-utterance, and multi-turn conversational data that mirror infant experiences. DevCV Toolbox adapts all vision-related measures of the recently released NIH Baby Toolbox into a benchmark suite of ten multimodal tasks, covering spatial reasoning, memory, and vocabulary understanding aligned with early children's capabilities. Experimental results show that a compact model pretrained from scratch can achieve competitive performance on DevCV Toolbox, outperforming GPT-4o on some tasks. We hope the principled, unified BabyVLM-V2 framework will accelerate research in developmentally plausible pretraining of vision foundation models.

Why we think this paper is great for you:
The paper's exploration of foundational models and developmental learning provides a valuable perspective on building intelligent systems. This aligns with your interest in leveraging foundational models for product development.

AI Summary

• Artificial Intelligence (AI)-based knowledge extraction: The use of AI algorithms to extract relevant information from existing designs or databases, enabling automatic generation of new design proposals. [3]
• The integration of AI and DFM has the potential to revolutionize product development by improving efficiency and reducing costs. [3]
• The paper discusses the integration of artificial intelligence (AI) and design for manufacturing (DFM) to improve product development efficiency and reduce production costs. [2]

Abstract
The growing adoption of Industrial Internet of Things (IIoT) technologies enables automated, real-time collection of manufacturing process data, unlocking new opportunities for data-driven product development. Current data-driven methods are generally applied within specific domains, such as design or manufacturing, with limited exploration of integrating design features and manufacturing process data. Since design decisions significantly affect manufacturing outcomes, such as error rates, energy consumption, and processing times, the lack of such integration restricts the potential for data-driven product design improvements. This paper presents a data-driven approach to mapping and analyzing the relationship between design features and manufacturing process data. A comprehensive system architecture is developed to ensure continuous data collection and integration. The linkage between design features and manufacturing process data serves as the basis for developing a machine learning model that enables automated design improvement suggestions. By integrating manufacturing process data with sustainability metrics, this approach opens new possibilities for sustainable product development.

Why we think this paper is great for you:
This research focuses on using data to improve product development, a critical aspect of strategic product management. It offers a practical approach to leveraging data for sustainable innovation.

AI Summary

• The paper discusses the concept of agentic AI and its applications in various domains. [3]
• Agentic AI refers to autonomous intelligence that can perform complex tasks and make decisions on its own. [3]
• Agentic AI: Autonomous intelligence that can perform complex tasks and make decisions on its own. [3]
• The authors propose a framework for integrating large language models (LLMs) with blockchain smart contracts using Model Context Protocol (MCP). [2]
• The paper highlights the need for evaluating AI reasoning models in pediatric medicine and discusses the comparative analysis of O3-Mini and O3-Mini-High models. [1]

Abstract
Agentic AI marks a major shift in how autonomous systems reason, plan, and execute multi-step tasks. Unlike traditional single model prompting, agentic workflows integrate multiple specialized agents with different Large Language Models(LLMs), tool-augmented capabilities, orchestration logic, and external system interactions to form dynamic pipelines capable of autonomous decision-making and action. As adoption accelerates across industry and research, organizations face a central challenge: how to design, engineer, and operate production-grade agentic AI workflows that are reliable, observable, maintainable, and aligned with safety and governance requirements. This paper provides a practical, end-to-end guide for designing, developing, and deploying production-quality agentic AI systems. We introduce a structured engineering lifecycle encompassing workflow decomposition, multi-agent design patterns, Model Context Protocol(MCP), and tool integration, deterministic orchestration, Responsible-AI considerations, and environment-aware deployment strategies. We then present nine core best practices for engineering production-grade agentic AI workflows, including tool-first design over MCP, pure-function invocation, single-tool and single-responsibility agents, externalized prompt management, Responsible-AI-aligned model-consortium design, clean separation between workflow logic and MCP servers, containerized deployment for scalable operations, and adherence to the Keep it Simple, Stupid (KISS) principle to maintain simplicity and robustness. To demonstrate these principles in practice, we present a comprehensive case study: a multimodal news-analysis and media-generation workflow. By combining architectural guidance, operational patterns, and practical implementation insights, this paper offers a foundational reference to build robust, extensible, and production-ready agentic AI workflows.

Why we think this paper is great for you:
This paper provides a framework for building intelligent systems, aligning with your interest in AI applications. Understanding agentic workflows will be valuable for building intelligent solutions within your field.

AI Summary

• Bayesian statistics are the natural framework for AI risk, allowing risk estimates to be continuously updated as models are evaluated and new behaviors are observed. [3]
• Bayesian Networks (BNs) are graphical models that help represent and quantify probabilistic relationships among a set of variables, making them excellent for modeling pathways to harm that involve multiple interacting factors. [3]
• Copulas are a powerful tool for modeling statistical interdependence without assuming causality, useful for modeling systemic or cascading risks where the failure of one component correlates with the failure of others. [3]
• Monte Carlo simulation: A computational technique that models uncertainty by running thousands of trials, sampling from probability distributions to generate a range of possible outcomes and their frequencies. [3]
• Bayesian statistics: A framework for updating prior knowledge (expert belief) with new evidence, enabling learning and continuous risk estimation. [3]
• Bayesian Networks (BNs): Graphical models representing probabilistic relationships among variables, integrating expert knowledge with data to model complex causal chains and update probabilities as new evidence becomes available. [3]
• Copulas: Functions that separate marginal probability distributions from the structure of statistical interdependence, useful for modeling systemic or cascading risks. [3]
• The lack of historical data for novel AI capabilities and failure modes is a significant challenge in quantifying AI risk. [2]
• Expert elicitation: A method used when empirical data is sparse, involving querying specialists on the likelihood of specific events. [1]

Abstract
Rapidly advancing artificial intelligence (AI) systems introduce novel, uncertain, and potentially catastrophic risks. Managing these risks requires a mature risk-management infrastructure whose cornerstone is rigorous risk modeling. We conceptualize AI risk modeling as the tight integration of (i) scenario building$-$causal mapping from hazards to harms$-$and (ii) risk estimation$-$quantifying the likelihood and severity of each pathway. We review classical techniques such as Fault and Event Tree Analyses, FMEA/FMECA, STPA and Bayesian networks, and show how they can be adapted to advanced AI. A survey of emerging academic and industry efforts reveals fragmentation: capability benchmarks, safety cases, and partial quantitative studies are valuable but insufficient when divorced from comprehensive causal scenarios. Comparing the nuclear, aviation, cybersecurity, financial, and submarine domains, we observe that every sector combines deterministic guarantees for unacceptable events with probabilistic assessments of the broader risk landscape. We argue that advanced-AI governance should adopt a similar dual approach and that verifiable, provably-safe AI architectures are urgently needed to supply deterministic evidence where current models are the result of opaque end-to-end optimization procedures rather than specified by hand. In one potential governance-ready framework, developers conduct iterative risk modeling and regulators compare the results with predefined societal risk tolerance thresholds. The paper provides both a methodological blueprint and opens a discussion on the best way to embed sound risk modeling at the heart of advanced-AI risk management.

Why we think this paper is great for you:
Given the increasing focus on responsible AI, this paper offers insights into managing the risks associated with advanced AI systems. It's relevant to ensuring the safe and effective implementation of AI solutions.